Repository logo
  • English
  • Deutsch
  • Español
  • Français
  • Log In
    New user? Click here to register.Have you forgotten your password?

  • English
  • Deutsch
  • Español
  • Français
  • Log In
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • Research Outputs
  • Fundings & Projects
  • Researchers
  • Statistics
  1. Home
  2. Current Research Information System UV
  3. Publicaciones
  4. Computer-Aided Autism Diagnosis Via Second-Order Difference Plot Area Applied To Eeg Empirical Mode Decomposition
 
  • Details
Options

Computer-Aided Autism Diagnosis Via Second-Order Difference Plot Area Applied To Eeg Empirical Mode Decomposition

Journal
Neural Computing and Applications
Date Issued
2018-09-21
Author(s)
Enas Abdulhay
Maha Alafeef
Loai Alzghoul
Miral Al Momani
Rabah Al Abdi
N. Arunkumar
Victor Hugo C. de Albuquerque
Muñoz Soto, Roberto  
Facultad de Ingeniería  
DOI
10.1007/s00521-018-3738-0
WoS ID
WOS:000549646700012
Abstract
Autism spectrum disorder (ASD) is a name for a group of neurodevelopmental conditions that are characterized by some degree of impairment in social interaction, verbal and non-verbal communication, and difficulty in symbolic capacity and repetitive behaviors. The only protocol followed currently for ASD diagnosis is the qualitative behavioral assessment by experts through internationally established descriptive scaling standards. The assessment can, therefore, be affected by the degree of the evaluator experience as well as by the level of the descriptive standard robustness. This paper presents an EEG-based quantitative approach intended for automatic discrimination between children with typical neurodevelopment and children with ASD. The suggested work relies on second-order difference plot (SODP) area as a discriminative feature: First, every EEG channel in a 64 electrode cap—for every volunteer—is decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD). Next, the second-order difference plot for the first ten intrinsic mode functions—of every channel—is sketched. Third, the value of the elliptical area —for every plot—is calculated. The 95% confidence ellipse area is used as the discriminative feature. Fourth, paired t-student test is applied to the vectors consisting of discriminative feature values for counterpart channels/IMFs (e.g., channel FPz/IMF7 in autistic and neurotypical) for all volunteers. Finally, principal component analysis (PCA) and neural network (NN) are applied to the SODP area feature matrix for two-class classification (ASD and neurotypical). Moreover, the 3D mapping of EEG SODP area values was implemented and analyzed. The obtained results show that the conducted t-student tests yield values of less than 0.05, and that the NN two-class classification based on SODP features leads to a 94.4% accuracy, which indicates significant differences between SODP area values of children with neurotypical development and those diagnosed with ASD. The obtained results have also been emphasized by the analysis of the findings of the performed 3D mapping.
Subjects

Artificial Intelligen...

Computer Science, Art...

Software

OCDE Subjects

Natural Sciences::Phy...

Quartile (Date Issued)
Q2
License
acceso restringido

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback

Hosting & Support by

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science